Monitoring My Portfolio During a Volatile Market

Barry from TX posted over 3 years ago:

Charles, kudos on sharing your portfolio’s YTD performance data as a model to reinforce the paramount importance of the values of patience and discipline in managing portfolios over the long term. Values that veterans can uniquely appreciate as a way of life. Greetings to all my fellow veterans on this Veterans Day. Semper Fi to the Corps on its 224th anniversary yesterday. It was the CHOICE to serve that makes possible for all Americans to be able to improve their lives through access to free markets in a free country. Career veterans devote 20 to 30+ years of their lives in service to their country. High pay is not one of the expected benefits. Focus on the mission, not on the market, is expected. Moving frequently and living overseas are also expected. Many veterans lacked the time and money to invest until after they completed their service. They “lost” 2-3 decades as an investor. One of the basic principles of PRISM is to start investing as early as you can. If, for some reason -- including military service, you “wait” until age 40 or 50 before you start your investing career, you have reduced your terminal earnings as much as 50%. For example, under the Rule of 72 – (you can estimate an expected ROI by dividing the number 72 by the number of years you are invested) –and using a lower-then-average long-term market return of 7% (Siegel and others have found it was closer to 8%), investments at 7% double about every 10 years. That means uninvested veterans may have foregone from 200% to 400% of their total lifetime wealth accumulation. We all know what Lincoln first recognized as "the last full measure of devotion" as he dedicated the cemetery at Gettysburg in 1863. It's what Memorial Day is all about. I hope this simple exercise helps estimate the value of choosing “devotion to Duty, Honor, and Country” over the pursuit of “life, liberty and happiness” through economic wealth. “Sir, yes sir! Three bags full, sir!” (A phrase from the nursery rhyme, ‘Baa, baa, black sheep,’ understood by military people to be an acceptable humorous, sometimes sarcastic, response (used with different inflections to indicate various degrees willingness) to do something their superiors think to be a difficult, if not impossible, “ask.")

Barry from TX posted over 3 years ago:

I reread this article in Jan 2023. First of all, I used the advice to partition the annual portfolio evaluation cycle into two halves based on "best" and "worst" 6 months periods. Second, I remembered there was also a fascinating factoid in the same article. "Between 1926 and 2021, there were only two years when both large-company stocks and intermediate-term government bonds were both down in the same calendar year. Those years were 1931 and 1969." I guess we need to add 2022 to our "WOAT" -- Worst of All Time -- markets list. Statistical process control and aircraft maintenance, and aviation safety use a measure called "Mean Time Between Failures" to calculate and compare the relative magnitude and the predictability of adverse events. There are too few data points here to use MTBF. Benoit Mandelbrot - who was famous for his contention that large swings in financial markets are not random, due to "long-term dependencies" across time - invented a measure for such situations that can be used to determine if there is any causality in such a series -- R/S - Rescaled Range Analysis. Calculating this type of statistic is "way above my pay grade," but it seems smug to dismiss and ignore it. It points to the limitations of depending on a normal distribution to provide answers to how to predict "outlier" events somewhere beyond 6 standard deviations to the left of the mean. Labeling it a "Black Swan" just does not provide any additional understanding of how we can predict future events. In "Stocks for the Long Run!" Nobelist Jeremy Siegel says it took until 1953 or so -- about a quarter of a century -- for investors in the 1929 crash to get back to breakeven. At least, the 2022 losses are much smaller (about 3 times) than the 1929 losses. Let's hope the magnitudes of "outlier" market events are logarithmically scalable similar to Richter's scale here. But how would we know if the only tool we use to compare WOAT events is a normal distribution?

Barry from TX posted over 3 years ago:

I reread this article in Jan 2023. First of all, I used the advice to partition the annual portfolio evaluation cycle into two halves based on "best" and "worst" 6 months periods. Second, I remembered there was also a fascinating factoid in the same article. "Between 1926 and 2021, there were only two years when both large-company stocks and intermediate-term government bonds were both down in the same calendar year. Those years were 1931 and 1969." I guess we need to add 2022 to our "WOAT" -- Worst of All Time -- markets list. Statistical process control and aircraft maintenance, and aviation safety use a measure called "Mean Time Between Failures" to calculate and compare the relative magnitude and the predictability of adverse events. There are too few data points here to use MTBF. Benoit Mandelbrot - who was famous for his contention that large swings in financial markets are not random, due to "long-term dependencies" across time - invented a measure for such situations that can be used to determine if there is any causality in such a series -- R/S - Rescaled Range Analysis. Calculating this type of statistic is "way above my pay grade," but it seems smug to dismiss and ignore it. It points to the limitations of depending on a normal distribution to provide answers to how to predict "outlier" events somewhere beyond 6 standard deviations to the left of the mean. Labeling it a "Black Swan" just does not provide any additional understanding of how we can predict future events. In "Stocks for the Long Run!" Nobelist Jeremy Siegel says it took until 1953 or so -- about a quarter of a century -- for investors in the 1929 crash to get back to breakeven. At least, the 2022 losses are much smaller (about 3 times) than the 1929 losses. Let's hope the magnitudes of "outlier" market events are logarithmically scalable similar to Richter's scale here. But how would we know if the only tool we use to compare WOAT events is a normal distribution?